Many social sequences can be analyzed as independent linear chains of events. For example, a given firm's trajectory of growth can be analyzed as a set of developmental stages that may be similar to, but not connected to, other firms' experiences. A common goal in recent analyses of such phenomena is to describe multiple chains and compare them to each other, then classify them or describe their degree of dissimilarity (e.g., see Aisenbrey and Fasang 2010; Piccarreta 2017; Studer et al. 2011). Expanding on the interest of scholars in the relational nature of social phenomena, some social scientists (e.g. Bearman et al. 1999; Bearman and Stovel 2000; Bison 2014; Cornwell 2015) have begun to explore sequences in terms of sets of intersecting events that constitute a larger network of pathways, in which multiple sequence chains are inextricably entwined and not considered as separate. These larger structures of intersecting sequences can be called "sequencenetworks." See also Hamberger (2018) in this bundle. Researchers who study sequence-networks are often interested in characterizing the overall landscape of interconnectedness and intersection-considering these structures as whole standalone entities, opportunity structures (Merton 1996), or social systems (Parsons 1951). Visually speaking, this involves seeing sequences as a vast, integrated web which, in two-dimensional space, resembles a roadmap. The ordered nature of the phenomena that make up these structures naturally invites the use of existing sequence-analytic tools such as discrepancy analysis (Studer